Human Activity Recognition using wearable body sensor by machine learning approach

Abstract

The prevalence of electronics devices and the increase in computer resources, like networking, storage, accessibility and sensor capacity, have significantly improved the lives of humans. Now a days most smart devices have a number of strong sensing equipment, such as sensors for movement, position, connection and direction.Basically, movement or motion tracking sensors are commonly been using to classify the physical activities of humans. This has opened entryways for a wide range of and intriguing applications with regards to a numerous zones, for example, human healthcare well being and transportation, security system. In this point of view, this research gives a complete, best in class audit of the present circumstance of human activity recognition (HAR) approaches with regards to inertial sensors in electronic portable smartphone devices. Our research started by analyzing the principles of human activities and the entire historical events based on electronics deices such a smartphone, which demonstrate the development in this area over the past few years. Our approach concentrates on the introduction of the means of HAR arrangements with regards to sensors. We propose a methodology which incorporates traditional signal processing techniques with deep learning tools to robustly classify activities from wearable body sensor data. Our proposed methodology achieves a validation accuracy of 96.26% in the WISDM Dataset and is able to recognize human activity from wearable body sensor data robustly.